A 1 year record of fractional cloudiness at 10 min intervals was generated for the Cabauw Experimental Site for Atmospheric Research (CESAR) (51°58′N, 4°55′E) using an integrated assessment of five different observational methods. The five methods are based on active as well as passive systems and use either a hemispheric or column remote sensing technique. The 1 year instrumental cloudiness data were compared against a 30 year climatology of Observer data in the vicinity of CESAR (1971–2000). In the intermediate 2–6 octa range, most instruments, but especially the column methods, report lower frequency of occurrence of cloudiness than the absolute minimum values from the 30 year Observer climatology. At night, the Observer records fewer clouds in the 1–2 octa range than during the day, while the instrumental techniques registered more clouds. During daytime the Observer also records much more 7 octa cloudiness than the instruments. A reference algorithm was designed to derive a continuous and optimized record of fractional cloudiness. Output from individual instruments were weighted according to the cloud base height reported at the observation time; the larger the height, the lower the weight. The algorithm was able to provide fractional cloudiness observations every 10 min for 99.92% of the total period of 12 months (15 May 2008 to 14 May 2009).
Abstract. The height of the atmospheric boundary layer or mixing layer is an important parameter for understanding the dynamics of the atmosphere and the dispersion of trace gases and air pollution. The height of the mixing layer (MLH) can be retrieved, among other methods, from lidar or ceilometer backscatter data. These instruments use the vertical backscatter lidar signal to infer MLHL, which is feasible because the main sources of aerosols are situated at the surface and vertical gradients are expected to go from the aerosol loaded mixing layer close to the ground to the cleaner free atmosphere above. Various lidar/ceilometer algorithms are currently applied, but accounting for MLH temporal development is not always well taken care of. As a result, MLHL retrievals may jump between different atmospheric layers, rather than reliably track true MLH development over time. This hampers the usefulness of MLHL time series, e.g. for process studies, model validation/verification and climatology. Here, we introduce a new method pathfinder, which applies graph theory to simultaneously evaluate time frames that are consistent with scales of MLH dynamics, leading to coherent tracking of MLH. Starting from a grid of gradients in the backscatter profiles, MLH development is followed using Dijkstra's shortest path algorithm (Dijkstra, 1959). Locations of strong gradients are connected under the condition that subsequent points on the path are limited to a restricted vertical range. The search is further guided by rules based on the presence of clouds and residual layers. After being applied to backscatter lidar data from Cabauw, excellent agreement is found with wind profiler retrievals for a 12-day period in 2008 (R2 = 0.90) and visual judgment of lidar data during a full year in 2010 (R2 = 0.96). These values compare favourably to other MLHL methods applied to the same lidar data set and corroborate more consistent MLH tracking by pathfinder.
Abstract. A two-year measurement campaign of the ZephIR 300 vertical profiling continuous-wave (CW) focusing wind lidar has been carried out by the Royal Netherlands Meteorological Institute (KNMI) at the Cabauw site. We focus on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213-m tall meteorological mast. We find an overall availability of quality controlled wind lidar data of 97 % to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. The mean bias in the wind direction is within 2°, which is on the same order as the combined uncertainty in the alignment of the wind lidars and the mast wind vanes. The well-known 180° error in the wind direction output for this type of instrument occurs about 9 % of the time. A correction scheme based on data of an auxiliary wind vane at a height of 10 m is applied, leading to a reduction of the 180° error below 2 %. This scheme can be applied in real-time applications in case a nearby, freely exposed, mast with wind direction measurements at a single height is available.
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